scholarly journals Experiences from Real-World Deployment of Context-Aware Technologies in a Hospital Environment

2006 ◽  
pp. 369-386 ◽  
Author(s):  
Jakob E. Bardram ◽  
Thomas R. Hansen ◽  
Martin Mogensen ◽  
Mads Soegaard
Keyword(s):  
Real World ◽  
Hospital Environment ◽  
Context Aware

Do We Walk Differently at Home? A Context-Aware Gait Analysis System in Continuous Real-World Environments

2021 ◽  
Author(s):  
Nils Roth ◽  
Georg P. Wieland ◽  
Arne Kuderle ◽  
Martin Ullrich ◽  
Till Gladow ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Real World ◽  
Context Aware ◽  
Analysis System ◽  
At Home

Pervasive Computing in the Supermarket

2012 ◽  
pp. 172-185 ◽  
Author(s):  
Darren Black ◽  
Nils Jakob Clemmensen ◽  
Mikael B. Skov
Keyword(s):  
Empirical Study ◽  
Real World ◽  
Context Awareness ◽  
Empirical Evaluation ◽  
Context Aware ◽  
Uniform Pattern ◽  
User Attention ◽  
Context Aware System ◽  
Mobile Context ◽  
Interactive Experience

Shopping in the real world is becoming an increasingly interactive experience as stores integrate various technologies to support shoppers. Based on an empirical study of supermarket shoppers, the authors designed a mobile context-aware system called the Context-Aware Shopping Trolley (CAST). The purpose of CAST is to support shopping in supermarkets through context-awareness and acquiring user attention, thus, the authors’ interactive trolley guides and directs shoppers in the handling and finding of groceries. An empirical evaluation showed that shoppers using CAST behaved differently than shoppers using a traditional trolley. Specifically, shoppers using CAST exhibited a more uniform pattern of product collection and found products more easily while travelling a shorter distance. As such, the study finds that CAST supported the supermarket shopping activity.

Development and Analysis of an Enhanced Multi-Expert Knowledge Integration System for Designing Context-Aware Ubiquitous Learning Contents

2018 ◽  
Vol 16 (4) ◽  
pp. 31-53 ◽  
Author(s):  
Gwo-Haur Hwang ◽  
Beyin Chen ◽  
Shiau-Huei Huang
Keyword(s):  
Knowledge Acquisition ◽  
Real World ◽  
Domain Knowledge ◽  
Knowledge Integration ◽  
Expert Knowledge ◽  
Context Aware ◽  
Ubiquitous Learning ◽  
Integration System ◽  
Degree Of Acceptance ◽  
Multiple Experts

This article describes how in context-aware ubiquitous learning environments, teachers must plan a theme and design learning contents to provide complete knowledge for students. Knowledge acquisition, which is an approach for helping people represent and organize domain knowledge, has been recognized as a potential way of guiding teachers to develop real-world context-related learning contents. However, previous studies failed to address the issue that the learning contents provided by multiple experts or teachers might be redundant or inconsistent; moreover, it is difficult to use the traditional knowledge acquisition method to fully describe the complex real-world contexts and the learning contents. Therefore, in this article, a multi-expert knowledge integration system with an enhanced knowledge representation approach and Delphi method has been developed. From the experimental results, it is found that the teachers involved had a high degree of acceptance of the system. They believe that it can unify the knowledge of many teachers.

scholarly journals Extracting Entity Synonymous Relations via Context-Aware Permutation Invariance

2022 ◽  
Vol 17 (1) ◽  
pp. 1-17
Author(s):  
Nan Yan ◽  
Subin Huang ◽  
Chao Kong
Keyword(s):  
Real World ◽  
Relation Extraction ◽  
The Other ◽  
Context Aware ◽  
Important Work ◽  
Relational Context ◽  
Permutation Invariance ◽  
Level Statistics ◽  
Real World Datasets ◽  
Better Than

Discovering entity synonymous relations is an important work for many entity-based applications. Existing entity synonymous relation extraction approaches are mainly based on lexical patterns or distributional corpus-level statistics, ignoring the context semantics between entities. For example, the contexts around ''apple'' determine whether ''apple'' is a kind of fruit or Apple Inc. In this paper, an entity synonymous relation extraction approach is proposed using context-aware permutation invariance. Specifically, a triplet network is used to obtain the permutation invariance between the entities to learn whether two given entities possess synonymous relation. To track more synonymous features, the relational context semantics and entity representations are integrated into the triplet network, which can improve the performance of extracting entity synonymous relations. The proposed approach is implemented on three real-world datasets. Experimental results demonstrate that the approach performs better than the other compared approaches on entity synonymous relation extraction task.

Pervasive Computing in the Supermarket

2010 ◽  
Vol 2 (3) ◽  
pp. 31-43 ◽  
Author(s):  
Darren Black ◽  
Nils Jakob Clemmensen ◽  
Mikael B. Skov
Keyword(s):  
Empirical Study ◽  
Pervasive Computing ◽  
Real World ◽  
Context Awareness ◽  
Empirical Evaluation ◽  
Context Aware ◽  
User Attention ◽  
Context Aware System ◽  
Mobile Context ◽  
Interactive Experience

Shopping in the real world is becoming an increasingly interactive experience as stores integrate various technologies to support shoppers. Based on an empirical study of supermarket shoppers, the authors designed a mobile context-aware system called the Context-Aware Shopping Trolley (CAST). The purpose of CAST is to support shopping in supermarkets through context-awareness and acquiring user attention, thus, the authors’ interactive trolley guides and directs shoppers in the handling and finding of groceries. An empirical evaluation showed that shoppers using CAST behaved differently than shoppers using a traditional trolley. Specifically, shoppers using CAST exhibited a more uniform pattern of product collection and found products more easily while travelling a shorter distance. As such, the study finds that CAST supported the supermarket shopping activity.

ReCQ: Real-World Context-Aware Querying

2007 ◽  
pp. 248-262 ◽  
Author(s):  
Shun Hattori ◽  
Taro Tezuka ◽  
Hiroaki Ohshima ◽  
Satoshi Oyama ◽  
Junpei Kawamoto ◽  
...  
Keyword(s):  
Real World ◽  
Context Aware

Evaluating Mobile Applications in Virtual Environments

2013 ◽  
Vol 5 (4) ◽  
pp. 1-19 ◽  
Author(s):  
Ioannis Delikostidis ◽  
Thore Fechner ◽  
Holger Fritze ◽  
Ahmed Mahmoud AbdelMouty ◽  
Christian Kray
Keyword(s):  
Virtual Environments ◽  
Real World ◽  
Mobile Applications ◽  
Future Research ◽  
Context Aware ◽  
Middle Ground ◽  
Selection Strategies ◽  
Research Challenges ◽  
Evaluation Approaches ◽  
Specific Evaluation

Context plays a central role in mobile applications but is very difficult to control, and therefore, the evaluation of context-aware applications can be challenging. Traditionally, researchers had to choose either field-based or lab-based studies but recently, virtual environments have been proposed as a middle-ground between those two methods. In this paper, the authors review previous work on using virtual environments to evaluate mobile applications. the authors identify and classify different approaches to simulate specific aspects of the real world, and analyse their relative properties with respect to evaluating different facets of context-aware mobile applications. Based on this analysis, the authors derive criteria and selection strategies that can help researchers in picking specific evaluation approaches. The authors also point out a number of research challenges in this area as well as a number of promising areas for future research.

scholarly journals A Dynamic and Static Context-Aware Attention Network for Trajectory Prediction

2021 ◽  
Vol 10 (5) ◽  
pp. 336
Author(s):  
Jian Yu ◽  
Meng Zhou ◽  
Xin Wang ◽  
Guoliang Pu ◽  
Chengqi Cheng ◽  
...  
Keyword(s):  
Real World ◽  
Autonomous Driving ◽  
Attention Mechanism ◽  
Context Aware ◽  
Trajectory Prediction ◽  
Attention Network ◽  
Series Of Experiments ◽  
Real World Datasets ◽  
The Moment ◽  
Autonomous Driving System

Forecasting the motion of surrounding vehicles is necessary for an autonomous driving system applied in complex traffic. Trajectory prediction helps vehicles make more sensible decisions, which provides vehicles with foresight. However, traditional models consider the trajectory prediction as a simple sequence prediction task. The ignorance of inter-vehicle interaction and environment influence degrades these models in real-world datasets. To address this issue, we propose a novel Dynamic and Static Context-aware Attention Network named DSCAN in this paper. The DSCAN utilizes an attention mechanism to dynamically decide which surrounding vehicles are more important at the moment. We also equip the DSCAN with a constraint network to consider the static environment information. We conducted a series of experiments on a real-world dataset, and the experimental results demonstrated the effectiveness of our model. Moreover, the present study suggests that the attention mechanism and static constraints enhance the prediction results.

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